Nowadays high usage of users from virtual environments and their connection via social networks like Facebook, Instagram, and Twitter shows the necessity of finding out shared subjects in this environment more than before. There are several applications that benefit from reliable methods for inferring age and gender of users in social media. Such applications exist across a wide area of fields, from personalized advertising to law enforcement of reputation management. Text posts represent a large portion of user generated content, and contain information which can be relevant to discovering undisclosed user attributes, or investigating the honesty of self-reported age and gender. Because the highest rate of information exchanges is in text format, author identification from the aspects like age, gender, political and religious opinions from these contents will seem more considerable. Gender identification that could be useful in security and marketing, also answers the following question: given a short text document, can we identify if the author is a male or a female? This question is motivated by recent events where people faked their gender on the Internet. In this paper, author gender identification in blog’ s data is investigated. In this regard, four groups of features include syntactic features, word-based features, character-based features, and function words are employed. In addition, character n-gram features is used for improving the accuracy of classification. For evaluation of the proposed method, 3212 texts were collected from Technorati. com and blogger. com. Experimental results demonstrate that these types of features are practical. furthermore, a new classification method called "Bayesian Random Forest" is introduced. Each tree in Bayesian Random Forest is a Bayes tree. The results of experiment show that this method attains noticeable results in comparison with other classification algorithms such as Naï ve Bayes, Naï ve Bayes Tree, and Random Forest and it increases accuracy of gender identification to 89. 5%.

The growing use of social media and online communication to express opinions, exchange ideas, and also the expanding use of of this platforms by Persian users has increased Persian texts on the Web. This remarkable growth, along with abusive use of the writer's anonymity, reveals the need for the author's automatic identification system in this language. In this research, the purpose of the study is to investigate the factors affecting the identification of authors of Persian reviews produced by cell-phone buyers and also to evaluate supervised and unsupervised methods. The factors considered in this research include lexical, syntactic, semantic, structural, grammatical, text-specific, and specific to social networks. After extracting these features, selecting the best features is tested by four algorithms including feature correlation, gain ratio, OneR, and principal components analysis. In the following, K-means, EM and density-based clustering will be used for clustering and Bayesian network, random forest, and Bagging will be used for categorization. The evaluation of the above algorithms on Persian comments of Samsung phone buyers indicates that the best performance among the clustering algorithms is 59/16% obtained by the EM algorithm on top-15 features selected by OneR, while the random forest algorithm using top-90 features selected by gain ratio with 79/57% achieves the best performance among the classification algorithms. Also, the comparison of features showed that syntactic features had the most effect on the identification of the author of short texts, and then, lexical, text-specific, specific to social networks, structural, grammatical and semantic features, respectively.

Handwriting recognition has always been a challenge; therefore, it has attracted the attention of many researchers. The present study presents an offline system for the automatic detection of human handwriting under different experimental conditions. This system includes input data, image processing unit, and output unit. In this study, a right-to-left dataset is designed based on the standards of the American Society for Experiments and Materials (ASTM). An improved deep convolution neural network (DCNN) model based on a pre-trained network is designed to extract features hierarchically from raw handwritten data. A significant advantage in this study is the use of heterogeneous data. Another significant aspect of the present study is that the proposed DCNN model is independent of any particular language and can be used for different languages. The results show that the proposed DCNN model has a very good performance for identifying the author based on heterogeneous data.

Gigantic amount of textual data being transferred in web every day. Like other communities, cyberspace is vulnerable to attacks, false information and deception. It becomes increasingly important to design an efficient method to trace identity in this community. In order to investigate the problem of gender identification, we propose 48 features, and design three machine learning algorithms. The results of study showed that AD tree classifier had accuracy up to 73.8%.

Root-knot nematodes are one of the limiting factors in seedling production and new olive gardens establishment in Iran. In view of find resistance cultivars to root-knot nematodes, importance of careful identification of species and races are clarified. In order to investigate root-knot nematodes on olive, soil and root samples of gardens and nursery in Gorgan area were collected. In this study, Meloidogyne incognita was identified based on females and second stage juvenils morphometrical and morphological characters. Stylet length, body length of female nematode, tail and hyaline length of second stage juveniles were shown some differences with original description. In original description, body length of females was 500 to 723 micrometer (609 micrometer average), whereas in under consideration species body length was (512-751) 622 micrometer. In original description tail length of second stage juveniles was (45-52.2) 48.7 micrometer and hyaline was (6.3-13.5) 8.9 micrometer while in investigated species tail length and hyaline were (43.9-55.4) 52.1 and (10.6-14.2) 12.9 micrometer, respectively. In response to differential hosts, the nematode showed a good multiply on tobacco, tomato, pepper and watermelon plants and able to produce knot whereas response cotton and peanut plants were negative. On the basis of morphological and morphometrical characteristics of matured female, perineal pattern, second stage juveniles, and response to differential hosts of M. incognita race 2 was detected.

Numerous studies have corroborated the importance of metadiscourse in academic writing. As one of the main components of metadiscourse, stance has received fairly extensive attention recently. One of the questions in this regard (with conflicting results) has been the relationship between author gender and the use of stance markers. Moreover, the relationship between author count and stance markers in research articles has been extremely under-researched. Therefore, this study set out to investigate the relationship of author gender and author count with stance markers in the applied linguistics research articles. To this end, a corpus of 416 articles (with a total word count of over 2.6 million words) was used for the author count question. Of this large corpus, 199 articles were used for the gender-stance investigation. The normalized frequencies of stance markers were extracted using LancsBox corpus analysis software. Then, the obtained data was analyzed using Mann-Whitney test to shed light on the relationship of author gender and author count with authorial stance. Mixed results were obtained for the relationship of author count and authorial stance components (including hedges, boosters, self-mention markers, and attitude markers), while no significant result was obtained for the relationship between gender and stance.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

Identifying the constraining factors of production and yield gap is very important. Therefore; this research was performed to identify the production constraining factors of local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the actual and calculated potential yield were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The yield gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total yield gap. The yield gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total yield variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the yield gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, actual yield mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha yield gap . Mean relative yield and relative yield gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each restricting yield variables.

Faramarz - nameh is a collection of verses in praise of the chivalry of faramarz, the son of Rustam in India, put to verses in 7600 couplets and printed in Bombay in 1324 A.H/A.D.1906. Dr. Jalal Khaleqi Mutlaq in the second part to his "Epic Studies" has undertaken to analyze the contents of this collection and in addition, with reference to verse directly quoted from the Faramarz - nameh has attempted to determine the author and the period in which it was put to verse. The present study is a continuation of Dr.Khaleqi" s discussions. This researcher has attempted to prove that the author of the Faramarz - nameh, Rafi al-Din Marzban - i Farsi was one of the poets of the 12(TH) century who belonged to the Fars province. The Faramarz- nameh was approximately put in to verse in the westem province of Iran approxmately around the year 555 A.H./A.D.1160.

Digital libraries may keep millions of citation records and bibliographic attributes such as title, authors' names, and the place of publication. Since the materials and contents in digital libraries are taken from diverse and distinct sources, there are some challenges regarding the use of digital libraries. One of the most important challenges is the ambiguity of authors' names. Although many methods have been proposed for solving the problem of ambiguous authors' names, their accuracy still must be enhanced. In this paper, an accurate method for author name disambiguation is proposed. It combines heuristic hierarchical clustering method and social networks to produce clusters with high accuracy. To evaluate the proposed method, an experiment is conducted using real dataset DBLP. The experimental results show that the accuracy can be enhanced using the proposed method.